Managed storage unit shutdown in a distributed storage network

ABSTRACT

A method begins by a load balancing module of a distributed storage network (DSN) receiving status information from a set of DSN processing units and selecting a DSN processing unit from the set of DSN processing units to process the data access request based on the status information. The method continues with the load balancing module transmitting, by the data access request to the DSN processing unit selected to process a data access request and receiving an indication of unfavorable performance from the DSN processing unit. The method continues with the load balancing module cancelling the data access request and receiving a second indication from the DSN processing unit, wherein the second indication indicates favorable performance.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. §120 as a continuation-in-part of U.S. Utility applicationSer. No. 15/345,172, entitled “STORING DATA IN A DIRECTORY-LESSDISPERSED STORAGE NETWORK,” filed Nov. 7, 2016, which claims prioritypursuant to 35 U.S.C. §121 as a divisional of U.S. Utility applicationSer. No. 14/307,625, entitled “STORING DATA IN A DIRECTORY-LESSDISPERSED STORAGE NETWORK”, filed Jun. 18, 2014, now U.S. Pat. No.9,495,118, issued on Nov. 15, 2016, which claims priority pursuant to 35U.S.C. §119(e) to U.S. Provisional Application No. 61/860,498, entitled“DISPERSED STORAGE AND COMPUTING NETWORK COMPONENTS AND OPTIMIZATIONS”,filed Jul. 31, 2013, all of which are hereby incorporated herein byreference in their entirety and made part of the present U.S. UtilityPatent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This present disclosure relates generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9A is a schematic block diagram of another embodiment of adispersed storage network (DSN) system in accordance with the presentdisclosure;

FIG. 9B is a diagram illustrating an example of load-balancing inaccordance with the present disclosure;

FIG. 10A is a schematic block diagram of another embodiment of adistributed storage (DS) execution unit in accordance with the presentdisclosure;

FIG. 10B is a diagram illustrating an example of memory utilization inaccordance with the present disclosure;

FIG. 10C is a diagram illustrating another example of memory utilizationin accordance with the present disclosure;

FIG. 10D is a flowchart illustrating an example of updating memoryutilization information in accordance with the present disclosure;

FIG. 10E is a flowchart illustrating example ways to identify slicesneeding a rebuild in accordance with the present disclosure;

FIG. 10F is a flowchart illustrating another example of updating memoryutilization information;

FIG. 10G is a schematic block diagram illustrating an example DS clientmodule structure for memory utilization;

FIG. 11A is a schematic block diagram of another embodiment of adispersed storage network (DSN) system in accordance with the presentdisclosure;

FIG. 11B is a diagram illustrating an example of generating a slice namein accordance with the present disclosure;

FIG. 11C is a flowchart illustrating an example of co-locating storageof data in accordance with the present disclosure;

FIG. 11D is a flowchart illustrating one example of obtaining theplurality of sets of encoded data slices to be co-located; and

FIG. 11E is a schematic block diagram of another embodiment of adispersed storage network (DSN) system in accordance with the presentdisclosure.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 and 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data (e.g., data 40) as subsequently described withreference to one or more of FIGS. 3-8. In this example embodiment,computing device 16 functions as a dispersed storage processing agentfor computing device 14. In this role, computing device 16 dispersedstorage error encodes and decodes data on behalf of computing device 14.With the use of dispersed storage error encoding and decoding, the DSN10 is tolerant of a significant number of storage unit failures (thenumber of failures is based on parameters of the dispersed storage errorencoding function) without loss of data and without the need for aredundant or backup copies of the data. Further, the DSN 10 stores datafor an indefinite period of time without data loss and in a securemanner (e.g., the system is very resistant to unauthorized attempts ataccessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The managing unit 18 creates and stores user profile information (e.g.,an access control list (ACL)) in local memory and/or within memory ofthe DSN memory 22. The user profile information includes authenticationinformation, permissions, and/or the security parameters. The securityparameters may include encryption/decryption scheme, one or moreencryption keys, key generation scheme, and/or data encoding/decodingscheme.

The managing unit 18 creates billing information for a particular user,a user group, a vault access, public vault access, etc. For instance,the managing unit 18 tracks the number of times a user accesses anon-public vault and/or public vaults, which can be used to generate aper-access billing information. In another instance, the managing unit18 tracks the amount of data stored and/or retrieved by a user deviceand/or a user group, which can be used to generate a per-data-amountbilling information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment (i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9A is a schematic block diagram of another embodiment of adispersed storage network (DSN) system that includes the DSN memory 22of FIG. 1, a set of storage units 1-N, where each storage unit includesthe computing device 16 of FIG. 1, and a load-balancing module 498. TheDSN memory 22 includes the storage unit set 438. The storage unit set438 includes a set of storage units 36 of FIG. 1.

The system functions to store data 500 as a plurality of sets of encodeddata slices 504 in the storage unit set 438. The load-balancing module498 selects one of the computing devices, based on resource statusinformation 502 from the computing devices, to encode the data 500 usinga dispersed storage error coding function to produce the plurality ofsets of encoded data slices 504 for storage in the storage unit set 438.The resource status information 502 includes one or more of an indicatorof a time frame of availability, an indicator of a time frame ofunavailability, a time frame for a scheduled software update, a timeframe for a scheduled new hardware addition, an error message, amaintenance schedule, a communications error rate, and a storage errorrate.

In an example of operation, a computing device determines to at leasttemporarily suspend operations. The determining may be based on one ormore of adding new software, activating new hardware, recovering from astorage error, recovering from a communications error, receiving asuspend request, and interpreting the maintenance schedule. Thecomputing device continues to perform a slice access activity withregards to pending data access requests associated with the computingdevice. The load-balancing module 498 receives a new data accessrequest. The load-balancing module 498 determines availability of eachof the computing devices based on one or more of receiving resourcestatus information 502, initiating a query, receiving an error message,and detecting an unfavorable performance (e.g., detecting slow responselatency). The load-balancing module 498 selects the computing devicewhen the availability (e.g., previously known availability) of thecomputing device compares favorably to availability of other computingdevices. The load-balancing module 498 forwards the data access requeststo the computing device.

While suspending operations, the computing devices indicates theunfavorable performance to the load-balancing module. The indicatingunfavorable performance includes at least one of ignoring the request,sending a late unfavorable response, issuing unfavorable resource statusinformation, and ignoring resource status requests from theload-balancing module. The load-balancing module 498 interprets theindication to determine that the data access request is to bereassigned. The load-balancing module 498 un-selects the computingdevice from the data access assignment. For example, the load-balancingmodule sends a cancellation message to the computing device and selectsanother computing device and sends the data access request to the othercomputing device.

FIG. 9B is a diagram illustrating an example of load-balancing. Themethod includes step 506 where a computing device determines totemporarily suspend operations. The method continues at step 508 wherethe computing device continues to execute pending operations. Forexample, the computing device continues to process previously accepteddata access requests. The method continues at step 510 where aload-balancing module receives a data access request. The methodcontinues at step 512 where the load-balancing module assessesavailability of a set of computing devices that includes the computingdevice. The assessing includes producing availability information basedon one or more of interpreting performance indicators, receivingresource status information, initiating a query, receiving an errormessage, and detecting favorable performance.

The method continues at step 514 where the load-balancing module selectsthe computing device for execution of the data access request. Forexample, the load-balancing module selects the computing device whenavailability of the computing device compares more favorably toavailability of other computing devices. The method continues at step516 where the load-balancing module forwards the data access request tothe computing device.

The method continues at step 518 where the computing device indicatesunfavorable performance. For example, the computing device ignores thedata access requests. As another example, the computing device waits adelay time period before sending a data access response causing theload-balancing module to interpret the data access response as a latedata access response associated with unfavorable performance. As yetanother example, the computing device delays responses associated withprevious accepted data access requests. The method continues at step 520where the load-balancing module detects the indicated unfavorableperformance. For example, the load-balancing module detects theindicated unfavorable performance when the data access response was notreceived within a desired response timeframe.

The method continues at step 522 where the load-balancing moduleun-selects the computing device for execution of the data accessrequest. The un-selecting includes one or more of sending a cancellationmessage to the computing device, selecting another computing device forthe data access request, and assigning the other computing device thedata access request.

The method continues at step 524 where the computing device determinesto resume operations. The determining may be based on one or more ofdetecting that new software is operational, detecting that new hardwareis operational, detecting that an error condition has cleared, anddetecting that a level of pending data access requests has fallen belowa low data access request threshold level. The method continues at step526 where the computing device indicates favorable performance. Forexample, the computing device generates data access responses inaccordance with desired data access response timing. As another example,the computing device responds to all data access requests. As yetanother example, the computing device sends favorable resource statusinformation to the load-balancing module.

FIG. 10A is a schematic block diagram of another embodiment of a storageunit (SU) 36 that includes the distributed storage (DS) client module 34and one or more memory devices 88. The memory includes a plurality ofportions of memory associated with different utilizations. The portionsmay be physical memory or virtual memory space. The plurality ofportions includes one or more portions utilized for slices memory 606,utilized for rebuilt slices memory 608, reserved for rebuilt slicesmemory 610, and un-utilized memory 612. The un-utilized memory 612 isassociated with available storage capacity, where the available storagecapacity may be calculated as a memory size minus memory used for eachof the utilized for slices memory 606, memory used for the utilized forrebuilt slices memory 608, and memory used for the reserved for rebuiltslices memory 610.

The storage unit 36 functions to store encoded data slices 600 in theutilized for slices memory 606 and store rebuilt encoded data slices 602in the utilized for rebuilt slices memory 608. The DS client module 34may obtain the rebuilt encoded data slices by at least one of: receivingthe rebuilt encoded data slices and generating the rebuilt encoded dataslices by retrieving representations of encoded data slices from adecode threshold number of other storage units 36. When encoded dataslices are to be stored, the DS client module 34 determines whethersufficient available storage capacity of the un-utilized memory isavailable for utilization for slices memory. For instance, the DS clientmodule compares a size of an encoded data slice for storage to the sizeof the un-utilized memory. The DS client module indicates that storagespace is available when the size of the encoded data slice is less thanthe size of the un-utilized memory. The DS client module 34 maydetermine the size of the reserved for rebuilt slices memory based onidentifying encoded data slices to be rebuilt. The identifying includesat least one of detecting a slice error and receiving an indication ofthe slice error.

In an example of operation, the DS client module 34 identifies aplurality of encoded data slices requiring rebuilding. The DS clientmodule 34 determines an amount of reserve memory 610 required forstorage of rebuilt slices for the identified plurality of encoded dataslices requiring rebuilding. The determining may include exchangingmemory utilization information 604 with at least one other Storage unit,where the exchanging includes receiving an amount of memory required foran encoded data slice associated with, for example, a slice error. TheDS client module 34 updates the memory utilization information toinclude the amount of reserve memory required. The memory utilizationinformation includes one or more of size of the utilized for slicesmemory, size of the utilized for rebuilt slices memory, size of thereserved for rebuilt slices memory, and size of the un-utilized memory.The DS client module 34 outputs the memory utilization information 604to one or more of a computing device, a managing unit, and a userdevice.

The DS client module 34 obtains rebuilt encoded data slices (e.g.,receives, generates) and stores the rebuilt encoded data slices in theutilized for rebuilt encoded data slices memory. Accordingly, the DSclient module updates the reserved for rebuilt slices memory by asimilar memory size amount as storage of the rebuild encoded data slices(e.g., lowers size of reserved for rebuilt slices memory and raises sizefor utilized for rebuilt slices memory). The DS client module updatesthe memory utilization information and may output the updated memoryutilization information.

FIGS. 10B-C are diagrams illustrating examples of memory utilization fora series of times frames, where each timeframe indicates an amount ofmemory utilized for slices, rebuilt slices, reserved for rebuilt slices,unutilized, and a total amount of memory capacity. The total amount ofmemory capacity remains constant over the time intervals. In particular,FIG. 10B illustrates examples of the memory utilization 614 for a firstset of time intervals T1-5. At T1, stored slices use 300 TB of memoryspace of a total capacity of 500 TB of memory space leaving 200 TB ofunutilized memory space. At T2, 50 TB of slices for rebuilding aredetected such that reserved for rebuilding is incremented by 50 TB andunutilized memory space is lowered by 50 TB from 200 TB to 150 TB. AtT3, a first 20 TB of rebuilt slices are obtained and stored such thatthe reserved memory space for rebuilt slices is lowered by 20 TB from 50TB to 30 TB. At T4, a remaining 30 TB of rebuilt slices are obtained andstored such that the reserve memory space rebuilt slices is lowered byanother 30 TB from 30 TB two 0 TB and the rebuilt slices is raised tobuy 30 TB from 20 TB to 50 TB. At T5, the rebuilt slices are moved tothe memory space for slices thus raising the rebuilt slices by 50 TBfrom 300 TB to 350 TB. Utilized memory includes the combination 615 ofutilized for slices memory 606, memory used for the utilized for rebuiltslices memory 608, and memory used for the reserved for rebuilt slicesmemory 610.

FIG. 10C continues the examples of memory utilization 616 for second setof time intervals T6-T10. The example begins at time interval T6 whichis equivalent to memory utilization of T5. At T7, 100 TB of new slicesare stored thus raising the memory utilization of slices from 350 TB to450 TB and lowering the unutilized memory space from 150 TB to 50 TB. AtT8, 50 TB of slices for rebuilding is detected such that memory space ofreserved for rebuilding is incremented by 50 TB from zero to 50 TB andmemory space of unutilized is lowered by 50 TB from 50 TB two 0 TB.Requests for storage of new slices are rejected since the memory spaceof the unutilized memory is zero. At T9, 50 TB of rebuilt slices arereceived and stored in the memory space of the rebuilt slices thusraising the rebuilt slices from 0 TB to 50 TB and lowering the memoryspace for rebuilt slices from 50 TB to 0 TB. At T10, the slices of thememory space rebuilt slices is considered part of the memory space ofslices thus raising the memory space of the slices from 450 TB to 500 TBand lowering the memory space of the rebuilt slices from 50 TB to 0 TB.As such, the memory storage space is full and subsequent request forstorage of slices or rebuilt slices shall be rejected.

FIG. 10D is a flowchart illustrating an example of updating memoryutilization information. The method begins at step 618 where aprocessing module (e.g., of a distributed storage DS client module)identifies a plurality of encoded data slices requiring rebuilding. Asfurther delineated in FIG. 10E (flowchart illustrating example ways toidentify slices needing a rebuild), the identifying includes at leastone of: receiving an error message 632 (e.g., no slices detected forrebuild, no access to rebuild information, not enough space to rebuild,etc.); receiving a rebuilding request 634 (e.g., to rebuild specificdata slices or range of data slices); detecting missing or corruptedencoded data slices by comparing a list of locally stored encoded dataslices (or range of slices) to a list of remotely stored encoded dataslices (or range of slices) associated with the locally stored encodeddata slices to identify missing slices or detecting unfavorable sliceintegrity (e.g., corrupted slices); monitoring downloads 638 to the DSmemory meeting minimum read/write (R/W) width thresholds but less than afull pillar width (successful download, but not all slices abovethreshold successfully downloaded); determining 640 when DSN read/write(R/W) requests occur for the plurality of encoded data slices andcomparing to known times of inaccessibility for the DS memory storingthe plurality of encoded data slices (e.g., DS memory was down formaintenance when original slice R/W request occurred); and queryingvaults related to the plurality of encoded data slices 641 to determineone or more missing or corrupted encoded data slices (e.g., other vaultssharing the same data slices may have a list or copies which include themissing or corrupted data slices).

The rebuilding of the plurality of encoded data slices is, in oneembodiment, queued for at least one of individual, group, or batchprocessing and the processing will be performed at a significant timedelay from the queuing. As the rebuild processing may occur in thefuture, the embodiments of FIGS. 10A-G, ensure that memory space is setaside for rebuilds such that interceding requests for memory slicestorage will not over utilize memory needed for the rebuild before ithas a chance to occur.

The method continues at the step 620 where the processing moduledetermines an amount of memory space to reserve for the plurality ofencoded data slices requiring rebuilding. The determining includesidentifying slice sizes based on at least one of initiating a slice sizequery with regards to the remotely stored encoded data slices, receivinga query response, and performing a local lookup based on a slice name.

The method continues at step 622 where the processing module updatesmemory utilization information to include the amount of memory space toreserve. For example, the processing module increments an amount ofmemory reserved for rebuilt slices by the amount of memory space toreserve and decrements unutilized memory space by the amount of memoryspace to reserve. The method continues at step 624 where the processingmodule sends the memory utilization information to at least one of astoring entity and a managing unit. The sending may further includedetermining whether a sum of an amount of memory utilized for slices, anamount of memory utilize for rebuilt slices, and an amount of memoryreserved for rebuilt slices is greater than a capacity of memory. Whenthe sum is greater, the processing module may further send an indicationthat the memory is full.

The method continues at step 626 where the processing module obtainsrebuilt encoded data slices (e.g., received, generate). The methodcontinues at step 628 where the processing module stores the rebuiltencoded data slices in a local DS memory. The method continues at step630 where the processing module updates the amount of memory space toreserve for remaining encoded data slices requiring rebuilding. Theupdating includes determining an amount of memory space utilized tostore the obtained rebuilt encoded data slices, incrementing the amountof memory space utilized for rebuilt slices by the amount of memoryspace utilized to store the obtained rebuilt encoded data slices, anddecrementing the amount of memory space reserved for rebuilt slices bythe amount of memory space utilized to store the obtained rebuiltencoded data slices. The updating may further include updating thememory space utilized for slices to include the amount of memory spaceutilized to store the obtained rebuilt encoded data slices anddecrementing the amount of memory space utilized to store the rebuildencoded data slices. The method loops back to the step where theprocessing module updates the memory utilization information.

FIG. 10F is a flowchart illustrating another example of updating memoryutilization information. The method begins at step 642 where aprocessing module (e.g., DS integrity processing unit 20) attempts toretrieve a plurality of encoded data slices from a DS memory to performan integrity check. Slices are retrieved based on any of: list(s) ofslice addresses, list(s) of names, range(s) of slice addresses andrange(s) of slice names. In step 644, it is determined if the encodeddata slices were retrieved during the attempted retrieval. In step 646,for encoded data slices that were not received and/or not listed, theyare flagged as missing slices. For retrieved encoded data slices, theyare checked for errors due to data corruption, outdated version, etc. Instep 648, if a slice includes an error, it is flagged as a ‘bad’ slice.Bad and/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices.

The rebuilding of the plurality of encoded data slices is, in oneembodiment, queued for at least one of individual, group, or batchprocessing and the processing will be performed at a significant timedelay from the queuing. As the rebuild processing may occur in thefuture, the embodiments of FIGS. 11A-G, ensure that memory space is setaside for rebuilds such that interceding requests for memory slicestorage will not over utilize memory needed for the rebuild before ithas a chance to occur.

The method continues at the step 650 where the processing moduledetermines an amount of memory space to reserve for the plurality ofencoded data slices requiring rebuilding. The determining includesidentifying slice sizes based on at least one of initiating a slice sizequery with regards to the remotely stored encoded data slices, receivinga query response, and performing a local lookup based on a slice name.

The method continues at step 652 where the processing module updatesmemory utilization information to include the amount of memory space toreserve. For example, the processing module increments an amount ofmemory reserved for rebuilt slices by the amount of memory space toreserve and decrements unutilized memory space by the amount of memoryspace to reserve. The method continues at step 653 where the processingmodule sends the memory utilization information to at least one of astoring entity (e.g., storage/vault peers), user units and a managingunit. The sending may further include determining whether a sum of anamount of memory utilized for slices, an amount of memory utilize forrebuilt slices, and an amount of memory reserved for rebuilt slices isgreater than a capacity of memory. When the sum is greater, theprocessing module may further send an indication that the memory isfull.

The method continues at step 654 where the processing module obtainsrebuilt encoded data slices (e.g., received, generated) and stores, instep 656, the rebuilt encoded data slices in a local DS memory. Themethod continues at step 657 where the processing module updates theamount of memory space to reserve for remaining encoded data slicesrequiring rebuilding. The updating includes determining an amount ofmemory space utilized to store the obtained rebuilt encoded data slices,incrementing the amount of memory space utilized for rebuilt slices bythe amount of memory space utilized to store the obtained rebuiltencoded data slices, and decrementing the amount of memory spacereserved for rebuilt slices by the amount of memory space utilized tostore the obtained rebuilt encoded data slices. The updating may furtherinclude updating the memory space utilized for slices to include theamount of memory space utilized to store the obtained rebuilt encodeddata slices and decrementing the amount of memory space utilized tostore the rebuild encoded data slices.

FIG. 10G is a schematic block diagram illustrating an example DS clientmodule 34 structure for memory utilization. DS client module 34 mayinclude a plurality of processing modules (or sub-modules) to performone or more steps of the embodiments of FIGS. 10A-F. While this exampleis shown as seven separate modules, the modules may becombined/separated into any number of modules (local or remote) tocomplete the various steps and functions of the various embodiments ofFIGS. 10A-F.

As shown, identify module 34-1 identifies a plurality of encoded dataslices that require rebuilding, wherein rebuilding of the plurality ofencoded data slices is queued for at least one of individual, group, orbatch processing and the processing will be performed at a significanttime delay from the queuing. Determine module 34-2 determines an amountof memory required for storage of the rebuild encoded data slices forthe plurality of encoded data slices. Update module 34-3 updatesutilization information of the memory by allocating a portion ofavailable memory to the amount of memory required. Indicate module 34-4indicates the memory utilization (e.g., by sending the updatedutilization information 604 of the memory to at least one of a storingentity (e.g., other storage/vault peers) and a managing unit). Obtainmodule 34-5 obtains rebuilt data slices (e.g., from other good copies orrelated vaults or generates them from other encoded data slices). Storemodule 34-6 stores the rebuilt encoded data slices in the reservememory; and modify module 34-7 modifies the utilization information toreflect the stored rebuilt encoded data slices. Additional modules maybe included within DS client module 34 to perform additional tasks (forexample, but not limited to, passing encoded data slices to/from slicememory during non-rebuild write/read (W/R) operations). Alternatively,obtain module 34-5 and store module 34-6 may perform the receive andstore slices 600 tasks, respectively.

FIG. 11A is a schematic block diagram of another embodiment of adispersed storage network (DSN) system that includes the disbursingstorage (DS) processing unit 16 and the distributed storage network(DSN) module 22 of FIG. 1. The DSN module 22 includes at least twoStorage unit sets 1-2. Each storage unit set includes a set of storageunits 36 of FIG. 1. The system functions to store at least two dataobjects in a common storage unit set.

In an example of operation, the computing device 16 receives a dataobject 1 write request 700. The computing device 16 encodes data object1 using a dispersed storage error coding function to produce first sets(data object 1) of encoded data slices 700-1, 2, . . . n (where n equalsthe width (number of pillars) of the encoded data slice set). Thecomputing device 16 generates first sets of slice names for the firstsets of encoded data slices. The computing device 16 issues one or moresets of data object 1 write slice requests to a storage unit set 1 thatincludes the first sets of encoded data slices and the correspondingfirst sets of slice names, where the first sets of slice names fallwithin a range of slice names associated with the storage unit set 1.

With data object 1 stored in the first set of storage units 36, thecomputing device 16 receives a data object 2 co-locate write request 702with regards to storing a second data object in the same set of storageunits 36 as the first data object (e.g., in the storage unit set 1). Thedata object 2 co-locate write request includes a data identifier (ID) ofthe data object to be co-located with (e.g., a data ID of the dataobject 1), a data ID of the second data object (e.g., the data object 2to be co-located), and may include the data (e.g., data object 2) to beco-located when it is not already stored within the DSN module 22.

When the data object to be co-located (e.g., the second data object) isincluded in the data object 2 co-locate write request, the computingdevice 16 identifies the set of storage units 36 associated with thedata ID of data object 1 to be co-located with (e.g., the storage unitset 1). The determining includes accessing one or more of a directoryand a dispersed hierarchical index to identify a DSN address associatedwith the data ID of data object 1 to be co-located with and performing aDSN address-to-physical location table lookup to identify the set ofstorage units 36 associated with the data ID of data object 1 to beco-located with. Next, the computing device encodes the second dataobject (data object 2) to produce second sets of encoded data slices forstorage in the storage unit set 1. The computing device 16 generatessecond sets of slice names for the second sets of encoded data slices,where the second sets of slice names are based on the first sets ofslice names such that the second sets of slice names fall within a rangeof slice names associated with a range of slice names associated withthe set of storage units 36 associated with the data ID of data object 1to be co-located with. computing device 16 issues data object 2 writeslice requests to the set of storage units 36 associated with the dataID of the data object to be co-located with (e.g., to storage unit set1), where the data object 2 write slice requests includes the secondsets of encoded data slices.

When the data object to be co-located is not included in the data object2 co-locate write request, the computing device 16 determines whetherthe data object to be co-located is already co-located. The determiningincludes the computing device 16 identifying the storage unit setassociated with storage of the second data object and comparing theidentity to the identity of the Storage unit set associated with storageof the first data object. When data object 2 to be co-located is notalready co-located (e.g., with data object 1), the computing device 16recovers data object 2 from the storage unit set associated with storageof the second data object (e.g., from storage unit set 2). Therecovering includes issuing data object 2 read slice requests 704 to thestorage unit set associated with storage of the second data object andreceiving the second sets of encoded data slices (e.g., received fromstorage unit set 2). Next, the computing device 16 issues the dataobject 2 write slice requests to the set of storage units 36 associatedwith the data ID of the data object 1 to be co-located with (e.g., tostorage unit set 1), where the data object 2 write slice requestsincludes the received second sets of encoded data slices and thecorresponding second sets of slice names.

FIG. 11B is a diagram illustrating an example of generating an updatedslice name for a previously stored encoded data slice of a second dataobject to be co-located with one or more encoded data slices of a firstdata object. The slice name 706 has a structure that includes a sliceindex field 708, a vault identifier (ID) field 710, a generation field712, an object number field 714, and a segment number field 716. Asubstantial number of the fields of the slice name structure of a slicename of the previously stored encoded data slice of the second dataobject are updated to be substantially aligned with corresponding fieldsof the slice name structure of a slice name of the one or more encodeddata slices of the first data object. For example, a vault ID fieldentry of the previous data object 2 slice 1 is updated to besubstantially the same as a vault ID field entry of data object 1 slice1. As another example, an object number field entry of the previous dataobject 2 slice 1 is updated based on an object number field entry of theprevious data object 2 slice 1 such that the slice name of the updateddata object 2 slice 1 falls within a range of slice names associatedwith storage of the first data object.

FIG. 11C is a flowchart illustrating an example of co-locating storageof data objects. The method begins at step 718 where a processing module(e.g., a distributed storage (DS) processing unit) receives a dataobject 2 co-locate write request to co-locate a data object 2 with adata object 1 to be co-located with. The write request includes one ormore of data identifiers (IDs) for the data object 2 to be co-locatedand the data object 1 to be co-located with. The method continues atstep 720 where the processing module obtains a plurality of sets ofencoded data slices for the data object 2 to co-locate. The obtainingincludes one of receiving, generating, and retrieving. When receiving,the processing module extracts the plurality of sets of encoded dataslices from the write request 700. When generating, the processingmodule encodes the data object 2 to be co-located using a dispersedstorage error coding function to produce the plurality of sets ofencoded data slices. When retrieving, the processing module identifiesprevious sets of slice names utilized to store the plurality of sets ofencoded data slices based on a data ID of the data object 2 to becomeco-located, issues one or more sets of read slice requests to apreviously utilized set of storage units where the one or more sets ofread slice requests includes the previous sets of slice names, andreceiving the plurality of sets of encoded data slices 704.

The method continues at the step 722 where the processing modulegenerates a plurality of sets of slice names for the plurality of setsof encoded data slices based on addressing information of the dataobject 1 to be co-located with. For example, the processing modulegenerates the plurality of sets of slice names to include a vault IDassociated with the data object to be co-located with and an objectnumber field entry that causes the generated plurality of sets of slicenames to fall within a slice name range that is associated with a set ofstorage units where the data object to be co-located with is stored.

The method continues at the step 724 where the processing module storesthe plurality of sets of encoded data slices in the set of storage unitsusing the generated plurality of sets of slice names. The storingincludes generating one or more sets of write slice requests thatincludes the plurality of sets of encoded data slices and the generatedplurality of sets of slice names and outputting the one or more sets ofread slice requests to the set of storage units. When storage of theplurality of sets of encoded data slices in the set of storage units isconfirmed, and when the plurality of sets of encoded data slices wereretrieved using the previous sets of slice names, the method continuesat the step 726 where the processing module deletes the plurality ofsets of encoded data slices utilizing the previous sets of slice names.For example, the processing module issues a set of delete slice requeststhat includes the previous sets of slice names to the previous utilizedset of storage units.

FIG. 11D is a flowchart illustrating one example of obtaining theplurality of sets of encoded data slices to be co-located. Theobtaining, step 720, includes multiple processing paths for receiving,generating, and retrieving the plurality of sets of encoded data slicesto be co-located (data object 2) based on the location of data object 2at the time of the request. When receiving, the processing moduleextracts in step 727 the ID of data object 1, ID of data object 2 and,if included with the request, the plurality of data object 2 sets ofencoded slices from the write request 700. When data object 2 to beco-located (e.g., the second data object) is included in the data object2 co-locate write request, the computing device 16 identifies, beginningwith step 730, the set of storage units 36 associated with data ID 1 ofthe data object to be co-located with (e.g., the storage unit set 1).The determining includes accessing one or more of a directory in step731 and a dispersed hierarchical index in step 732 to identify a DSNaddress associated with data object 1 ID to be co-located with andperforming a DSN address-to-physical location table lookup in step 734to identify the physical location (PL) address set of storage units 36associated with the data ID of the data object to be co-located with. Ifdata object 2 is not already encoded, it is encoded in step 729 using adispersed storage error coding function.

When the data object to be co-located is not included in the data object2 co-locate write request, the computing device 16 determines whetherthe data object to be co-located is already co-located. The determiningincludes comparing data object 2 PL to data object 1 PL. If they areco-located (data object 2 PL is stored within a range of addresses fordata object 1 PL) no further action is required. When data object 2 tobe co-located is not already co-located, the computing device 16recovers (reads), in step 736, the second data object from the storageunit set associated with storage of the second data object (e.g., fromstorage unit set 2).

FIG. 11E is a schematic block diagram of another embodiment of adispersed storage network (DSN) system in accordance with the presentdisclosure. Computing device 16 may include a plurality of processingmodules (or sub-modules) to perform one or more steps of the embodimentsof FIGS. 11A-D. While this example is shown as four separate modules,the modules may be combined or separated into any number of modules(local or remote) to complete the various steps and functions of thevarious embodiments of FIGS. 11A-D.

As shown, receive module 16-1 operates to receive a data objectco-locate write request. Obtain module 16-2 operates to obtain aplurality of sets of encoded data slices for a data object to co-locate.Generate module 16-3 operates to generate a plurality of sets of slicenames for the data object to co-locate based on another plurality ofsets of slice names associated with a data object to be co-located with.Store module 16-4 operates to store the plurality of sets of encodeddata slices in DS memory using the generated plurality of sets of slicenames for the data object co-locate.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present disclosure has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed disclosure. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed disclosure. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present disclosure may have also been described, at least in part,in terms of one or more embodiments. An embodiment of the presentdisclosure is used herein to illustrate the present disclosure, anaspect thereof, a feature thereof, a concept thereof, and/or an examplethereof. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process that embodies the presentdisclosure may include one or more of the aspects, features, concepts,examples, etc. described with reference to one or more of theembodiments discussed herein. Further, from figure to figure, theembodiments may incorporate the same or similarly named functions,steps, modules, etc. that may use the same or different referencenumbers and, as such, the functions, steps, modules, etc. may be thesame or similar functions, steps, modules, etc. or different ones.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present disclosure. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent disclosure have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a load balancing moduleof a dispersed storage network (DSN), the method comprises: receiving,at the load balancing module, status information from each DSNprocessing unit of a set of DSN processing units; based on the statusinformation, selecting, by the load balancing module, a DSN processingunit of the set of DSN processing units to process a data accessrequest; transmitting, by the load balancing module, the data accessrequest to the DSN processing unit selected to process a data accessrequest; receiving, at the load balancing module, a first indicationfrom the DSN processing unit, wherein the DSN processing unit continuesto process previously pending data access requests, and further whereinthe first indication indicates unfavorable performance; detecting, bythe load balancing module, the first indication from the DSN processingunit; based on the first indication, cancelling, by the load balancingmodule, the selecting the DSN processing unit to process the data accessrequest; and receiving, at the load balancing module, a secondindication from the DSN processing unit, wherein the second indicationindicates favorable performance.
 2. The method of claim 1, wherein thestatus information from each DSN processing unit of the set of DSNprocessing units includes at least one of an indicator of a time frameof availability, an indicator of a time frame of unavailability, a timeframe for a scheduled software update, a time frame for a scheduled newhardware addition, an error message, a maintenance schedule, acommunications error rate, and a storage error rate.
 3. The method ofclaim 1, wherein the first indication indicates unfavorable performancebased on the DSN processing unit determining to temporarily suspendoperation.
 4. The method of claim 3, wherein the determining, by the DSNprocessing unit to temporarily suspend operation is based on at leastone of adding new software, activating new hardware, recovering from astorage error, recovering from a communications error, receiving asuspend request, and interpreting a maintenance schedule.
 5. The methodof claim 1, wherein the previously pending data access requests includeone or more encoded data slice access activities.
 6. The method of claim1, wherein the selecting, by the load balancing module, a DSN processingunit of the set of DSN processing units to process a data access requestis further based on at least one of receiving resource statusinformation, initiating a query, receiving an error message, anddetecting an unfavorable performance state.
 7. The method of claim 1,wherein the selecting, by the load balancing module, a DSN processingunit of the set of DSN processing units to process a data access requestis further based on the DSN processing unit comparing favorably toavailability of other DSN processing units of the set of DSN processingunits.
 8. The method of claim 1, wherein the first indication indicatingunfavorable performance includes the DSN processing unit doing at leastone of ignoring the request, sending a late unfavorable response,issuing unfavorable resource status information, and ignoring resourcestatus requests from the load balancing module.
 9. The method of claim1, further comprising: after cancelling, by the load balancing module,the selecting the DSN processing unit to process the data access requestselecting another DSN processing unit to process the data accessrequest.
 10. A computing device comprises: an interface for interfacingwith a network; memory; and a processing module operably coupled to theinterface and to the memory, wherein the processing module is operableto: receive status information from each DSN processing unit of a set ofDSN processing units; based on the status information, select a DSNprocessing unit of the set of DSN processing units to process a dataaccess request; transmit the data access request to the DSN processingunit selected to process a data access request; receive a firstindication from the DSN processing unit, wherein the DSN processing unitcontinues to process previously accepted data access requests, andfurther wherein the first indication indicates unfavorable performance;detect the first indication from the DSN processing unit; based on thefirst indication, cancel the data access request for the DSN processingunit; and receive a second indication from the DSN processing unit,wherein the second indication indicates favorable performance.
 11. Thecomputing device of claim 10, wherein the status information from eachDSN processing unit of the set of DSN processing units includes at leastone of an indicator of a time frame of availability, an indicator of atime frame of unavailability, a time frame for a scheduled softwareupdate, a time frame for a scheduled new hardware addition, an errormessage, a maintenance schedule, a communications error rate, and astorage error rate.
 12. The computing device of claim 10, wherein thefirst indication indicates unfavorable performance based on a temporarysuspension of operation.
 13. The computing device of claim 12, whereinthe DSN processing unit has determined to temporarily suspend operationbased on at least one of adding new software, activating new hardware,recovering from a storage error, recovering from a communications error,receiving a suspend request, and interpreting a maintenance schedule.14. The computing device of claim 10, wherein the previously pendingdata access requests include one or more encoded data slice accessactivities.
 15. The computing device of claim 10, wherein the processingmodule is further operable to operable to select a DSN processing unitof the set of DSN processing units to process a data access requestfurther based on at least one of receiving resource status information,initiating a query, receiving an error message, and detecting anunfavorable performance state.
 16. The computing device of claim 10,wherein the processing module is further operable to operable to selecta DSN processing unit of the set of DSN processing units to process adata access request is further based on the DSN processing unitcomparing favorably to availability of other DSN processing units of theset of DSN processing units.
 17. The computing device of claim 10,wherein the first indication includes the DSN processing unit executingat least one of ignoring the request, sending a late unfavorableresponse, issuing unfavorable resource status information, and ignoringresource status requests from the computing device.
 18. The computingdevice of claim 10, further comprising: select another DSN processingunit to process the data access request.
 19. A computer readable storagemedium comprises: at least one memory section that stores operationalinstructions that, when executed by one or more processing resources ofa plurality of processing resources of one or more computing devices ofa distributed network, causes the one or more computing devices to:receive status information from each DSN processing unit of a set of DSNprocessing units; based on the status information, select a DSNprocessing unit of the set of DSN processing units to process a dataaccess request; transmit the data access request to the DSN processingunit selected to process a data access request; receive a firstindication from the DSN processing unit, wherein the DSN processing unitcontinues to process previously accepted data access requests, andfurther wherein the first indication indicates unfavorable performance;detect the first indication from the DSN processing unit; based on thefirst indication, cancel the data access request for the DSN processingunit; and receive a second indication from the DSN processing unit,wherein the second indication indicates favorable performance.
 20. Thecomputer readable storage medium of claim 19, wherein the statusinformation from each DSN processing unit of the set of DSN processingunits includes at least one of an indicator of a time frame ofavailability, an indicator of a time frame of unavailability, a timeframe for a scheduled software update, a time frame for a scheduled newhardware addition, an error message, a maintenance schedule, acommunications error rate, and a storage error rate.